# A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models

arXiv: https://arxiv.org/abs/2409.11631 · HTML: https://mlsanity.com/paper/2409.11631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Social physics](https://mlsanity.com/paper/2110.01866/) (2021)
- [COLIN: Planning with Continuous Linear Numeric Change](https://mlsanity.com/paper/1401.5857/) (2014)
- [Scalable Planning with Tensorflow for Hybrid Nonlinear Domains](https://mlsanity.com/paper/1704.07511/) (2017)
- [Optimal Mixed Discrete-Continuous Planning for Linear Hybrid Systems](https://mlsanity.com/paper/2102.08261/) (2021)
- [Reward Potentials for Planning with Learned Neural Network Transition Models](https://mlsanity.com/paper/1904.09366/) (2019)

## Abstract (arXiv)

A pandemic is the spread of a disease across large regions, and can have devastating costs to the society in terms of health, economic and social. As such, the study of effective pandemic mitigation strategies can yield significant positive impact on the society. A pandemic can be mathematically described using a compartmental model, such as the Susceptible Infected Removed (SIR) model. In this paper, we extend the solution equations of the SIR model to a state transition model with lockdowns. We formalize a metric hybrid planning problem based on this state transition model, and solve it using a metric hybrid planner. We improve the runtime effectiveness of the metric hybrid planner with the addition of valid inequalities, and demonstrate the success of our approach both theoretically and experimentally under various challenging settings.